1. Technical Field
This disclosure relates to text mining and content recommendations through automated analysis of user activities or events that may include on-line reading, writing, browsing, and/or navigation, for example.
2. Related Art
Search engine rankings may provide information about selected topics or answer specific questions. Unfortunately some search engines cannot return the latest information about a topic or question when that information has not had sufficient time to establish its cyber reputation. Additionally, many Web search services require user to compose their own Web queries. To receive sought after information, the search engines require the users to ask the right questions and convey those questions through a required language expression following a specific query structure. Besides its counter-intuitive nature, such queries directed to sought-after information may not exist.
Unfortunately, most of the search engines do not offer an integrated experience of one stop information shop for any user. Unlike known technology, the technology disclosed in the Detailed Description that follows, un-intrusively observes the user's own information consumption behaviors in time, automatically infers that user's information interests, and proactively recommends personally high-value content for the user under a given context of a certain information consumption mode of the user (such as 9 am Monday morning, later Sunday afternoon, or during Christmas holidays). Some of the systems described in the Detailed Description do not require a more tactic query formulation or manual information searching and end-user filtering. The new technology discloses recommendation techniques and focuses on proactively delivering relevant content to an end user in a personalized way.
Appendices 1-6 describe a personalized re-ranking algorithm.